Wu O, Benner T, Roccatagliata L, Zhu M, Schaefer PW, Sorensen AG, Singhal AB. Evaluating effects of normobaric oxygen therapy in acute stroke with MRI-based predictive models. Med Gas Res 2012;2(1):5.Abstract
    BACKGROUND: Voxel-based algorithms using acute multiparametric-MRI data have been shown to accurately predict tissue outcome after stroke. We explored the potential of MRI-based predictive algorithms to objectively assess the effects of normobaric oxygen therapy (NBO), an investigational stroke treatment, using data from a pilot study of NBO in acute stroke. METHODS: The pilot study of NBO enrolled 11 patients randomized to NBO administered for 8 hours, and 8 Control patients who received room-air. Serial MRIs were obtained at admission, during gas therapy, post-therapy, and pre-discharge. Diffusion/perfusion MRI data acquired at admission (pre-therapy) was used in generalized linear models to predict the risk of lesion growth at subsequent time points for both treatment scenarios: NBO or Control. RESULTS: Lesion volume sizes 'during NBO therapy' predicted by Control-models were significantly larger (P = 0.007) than those predicted by NBO models, suggesting that ischemic lesion growth is attenuated during NBO treatment. No significant difference was found between the predicted lesion volumes at later time-points. NBO-treated patients, despite showing larger lesion volumes on Control-models than NBO-models, tended to have reduced lesion growth. CONCLUSIONS: This study shows that NBO has therapeutic potential in acute ischemic stroke, and demonstrates the feasibility of using MRI-based algorithms to evaluate novel treatments in early-phase clinical trials.
    Wu O, Sumii T, Asahi M, Sasamata M, Ostergaard L, Rosen BR, Lo EH, Dijkhuizen RM. Infarct prediction and treatment assessment with MRI-based algorithms in experimental stroke models. J Cereb Blood Flow Metab 2007;27(1):196-204.Abstract
    There is increasing interest in using algorithms combining multiple magnetic resonance imaging (MRI) modalities to predict tissue infarction in acute human stroke. We developed and tested a voxel-based generalized linear model (GLM) algorithm to predict tissue infarction in an animal stroke model in order to directly compare predicted outcome with the tissue's histologic outcome, and to evaluate the potential for assessing therapeutic efficacy using these multiparametric algorithms. With acute MRI acquired after unilateral embolic stroke in rats (n=8), a GLM was developed and used to predict infarction on a voxel-wise basis for saline (n=6) and recombinant tissue plasminogen activator (rt-PA) treatment (n=7) arms of a trial of delayed thrombolytic therapy in rats. Pretreatment predicted outcome compared with post-treatment histology was highly accurate in saline-treated rats (0.92+/-0.05). Accuracy was significantly reduced (P=0.04) in rt-PA-treated animals (0.86+/-0.08), although no significant difference was detected when comparing histologic lesion volumes. Animals that reperfused had significantly lower (P<0.01) GLM-predicted infarction risk (0.73+/-0.03) than nonreperfused animals (0.81+/-0.05), possibly reflecting less severe initial ischemic injury and therefore tissue likely more amenable to therapy. Our results show that acute MRI-based algorithms can predict tissue infarction with high accuracy in animals not receiving thrombolytic therapy. Furthermore, alterations in disease progression due to treatment were more sensitively monitored with our voxel-based analysis techniques than with volumetric approaches. Our study shows that predictive algorithms are promising metrics for diagnosis, prognosis and therapeutic evaluation after acute stroke that can translate readily from preclinical to clinical settings.
    Wu O, Christensen S, Hjort N, Dijkhuizen RM, Kucinski T, Fiehler J, Thomalla G, Röther J, Østergaard L. Characterizing physiological heterogeneity of infarction risk in acute human ischaemic stroke using MRI. Brain 2006;129(Pt 9):2384-93.Abstract
    Viable tissues at risk of infarction in acute stroke patients have been hypothesized to be detectable as volumetric mismatches between lesions on perfusion-weighted (PWI) and diffusion-weighted magnetic resonance imaging (DWI). Because tissue response to ischaemic injury and to therapeutic intervention is tissue- and patient-dependent, changes in infarct progression due to treatment may be better detected with voxel-based methods than with volumetric mismatches. Acute DWI and PWI were combined using a generalized linear model (GLM) to predict infarction risk on a voxel-wise basis for patients treated either with non-thrombolytic (Group 1; n = 11) or with thrombolytic therapy (Group 2; n = 27). Predicted infarction risk for both groups was evaluated in four ipsilateral regions of interest: tissue acutely abnormal on DWI (Core), tissue acutely abnormal on PWI but normal on DWI that either infarcts (Recruited) or does not (Salvaged), and tissue normal on both DWI and PWI that does not infarct (Normal) by follow-up imaging > or = 5 days. The performance of the models was significantly reduced for the thrombolysed group compared with the group receiving standard treatment, suggesting an alteration in natural progression of the ischaemic cascade. Average GLM-predicted infarction risk values in the four regions were different from one another for both groups. GLM-predicted infarction risk in Salvaged tissue was significantly higher (P = 0.02) for thrombolysed patients than for non-thrombolysed patients, suggesting that thrombolysis rescued tissue with higher infarction risk than typically measured in tissue that spontaneously recovered. The observed spatial heterogeneity of GLM-predicted infarction risk values probably reflects the varying degrees of tissue injury and salvageability that exist after stroke. MRI-based algorithms may therefore provide a more sensitive means for monitoring therapeutic effects on a voxel-wise basis.
    Gottrup C, Thomsen K, Locht P, Wu O, Sorensen GA, Koroshetz WJ, Østergaard L. Applying instance-based techniques to prediction of final outcome in acute stroke. Artif Intell Med 2005;33(3):223-36.Abstract
    OBJECTIVE: Acute cerebral stroke is a frequent cause of death and the major cause of adult neurological disability in the western world. Thrombolysis is the only established treatment of ischemic stroke; however, its use carries a substantial risk of symptomatic intracerebral hemorrhage. A clinical tool to guide the use of thrombolysis would be very valuable. One of the major goals of such a tool would be the identification of potentially salvageable tissue. This requires an accurate prediction of the extent of infarction if untreated. In this study, we investigate the applicability of highly flexible instance-based (IB) methods for such predictions. METHODS AND MATERIALS: Based on information obtained from magnetic resonance imaging of 14 patients with acute stroke, we explored three different implementations of the IB method: k-NN, Gaussian weighted, and constant radius search classification. Receiver operating characteristics analysis, in particular area under the curve (AUC), was used as performance measure. RESULTS: We found no significant difference (P = 0.48) in performance for the optimal k-NN (k = 164, AUC = 0.814 +/- 0.001) and Gaussian weight (sigma = 0.17, AUC = 0.813 +/- 0.001) implementations, while they were both significantly better (P < 1 x 10(-6) for both) than the constant radius implementation (R = 0.28, AUC = 0.809 +/- 0.001). Qualitative analyses of the distribution of instances in the feature space indicated that non-infarcted instances tends to cluster together while infarcted instances are more dispersed, and that there may not exist a stringent boundary separating infarcted from non-infarcted instances. CONCLUSIONS: This study shows that IB methods can be used, and may be advantageous, for predicting final infarct in patients with acute stroke, but further work must be done to make them clinically applicable.
    Wu O, Koroshetz WJ, Ostergaard L, Buonanno FS, Copen WA, Gonzalez RG, Rordorf G, Rosen BR, Schwamm LH, Weisskoff RM, Sorensen AG. Predicting tissue outcome in acute human cerebral ischemia using combined diffusion- and perfusion-weighted MR imaging. Stroke 2001;32(4):933-42.Abstract
    BACKGROUND AND PURPOSE: Tissue signatures from acute MR imaging of the brain may be able to categorize physiological status and thereby assist clinical decision making. We designed and analyzed statistical algorithms to evaluate the risk of infarction for each voxel of tissue using acute human functional MRI. METHODS: Diffusion-weighted MR images (DWI) and perfusion-weighted MR images (PWI) from acute stroke patients scanned within 12 hours of symptom onset were retrospectively studied and used to develop thresholding and generalized linear model (GLM) algorithms predicting tissue outcome as determined by follow-up MRI. The performances of the algorithms were evaluated for each patient by using receiver operating characteristic curves. RESULTS: At their optimal operating points, thresholding algorithms combining DWI and PWI provided 66% sensitivity and 83% specificity, and GLM algorithms combining DWI and PWI predicted with 66% sensitivity and 84% specificity voxels that proceeded to infarct. Thresholding algorithms that combined DWI and PWI provided significant improvement to algorithms that utilized DWI alone (P=0.02) but no significant improvement over algorithms utilizing PWI alone (P=0.21). GLM algorithms that combined DWI and PWI showed significant improvement over algorithms that used only DWI (P=0.02) or PWI (P=0.04). The performances of thresholding and GLM algorithms were comparable (P>0.2). CONCLUSIONS: Algorithms that combine acute DWI and PWI can assess the risk of infarction with higher specificity and sensitivity than algorithms that use DWI or PWI individually. Methods for quantitatively assessing the risk of infarction on a voxel-by-voxel basis show promise as techniques for investigating the natural spatial evolution of ischemic damage in humans.